Overview

Dataset statistics

Number of variables55
Number of observations1118822
Missing cells33290792
Missing cells (%)54.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory469.5 MiB
Average record size in memory440.0 B

Variable types

Text10
Numeric21
Categorical14
Unsupported2
DateTime6
Boolean2

Alerts

k-135㎡초과 has constant value "70.0"Constant
사용허가여부 has constant value "True"Constant
거래유형 is highly imbalanced (87.2%)Imbalance
k-단지분류(아파트,주상복합등등) is highly imbalanced (87.0%)Imbalance
k-세대타입(분양형태) is highly imbalanced (55.0%)Imbalance
k-관리방식 is highly imbalanced (69.9%)Imbalance
경비비관리형태 is highly imbalanced (60.3%)Imbalance
청소비관리형태 is highly imbalanced (72.2%)Imbalance
기타/의무/임대/임의=1/2/3/4 is highly imbalanced (84.7%)Imbalance
관리비 업로드 is highly imbalanced (87.8%)Imbalance
해제사유발생일 has 1112839 (99.5%) missing valuesMissing
k-단지분류(아파트,주상복합등등) has 870691 (77.8%) missing valuesMissing
k-전화번호 has 870274 (77.8%) missing valuesMissing
k-팩스번호 has 872742 (78.0%) missing valuesMissing
단지소개기존clob has 1050240 (93.9%) missing valuesMissing
k-세대타입(분양형태) has 869563 (77.7%) missing valuesMissing
k-관리방식 has 869563 (77.7%) missing valuesMissing
k-복도유형 has 869890 (77.8%) missing valuesMissing
k-난방방식 has 869563 (77.7%) missing valuesMissing
k-전체동수 has 870630 (77.8%) missing valuesMissing
k-전체세대수 has 869563 (77.7%) missing valuesMissing
k-건설사(시공사) has 871058 (77.9%) missing valuesMissing
k-시행사 has 871254 (77.9%) missing valuesMissing
k-사용검사일-사용승인일 has 869696 (77.7%) missing valuesMissing
k-연면적 has 869563 (77.7%) missing valuesMissing
k-주거전용면적 has 869608 (77.7%) missing valuesMissing
k-관리비부과면적 has 869563 (77.7%) missing valuesMissing
k-전용면적별세대현황(60㎡이하) has 869608 (77.7%) missing valuesMissing
k-전용면적별세대현황(60㎡~85㎡이하) has 869608 (77.7%) missing valuesMissing
k-85㎡~135㎡이하 has 869608 (77.7%) missing valuesMissing
k-135㎡초과 has 1118495 (> 99.9%) missing valuesMissing
k-홈페이지 has 1005647 (89.9%) missing valuesMissing
k-등록일자 has 1107832 (99.0%) missing valuesMissing
k-수정일자 has 869608 (77.7%) missing valuesMissing
고용보험관리번호 has 913304 (81.6%) missing valuesMissing
경비비관리형태 has 870988 (77.8%) missing valuesMissing
세대전기계약방법 has 878747 (78.5%) missing valuesMissing
청소비관리형태 has 871178 (77.9%) missing valuesMissing
건축면적 has 869714 (77.7%) missing valuesMissing
주차대수 has 869714 (77.7%) missing valuesMissing
기타/의무/임대/임의=1/2/3/4 has 869563 (77.7%) missing valuesMissing
단지승인일 has 870286 (77.8%) missing valuesMissing
사용허가여부 has 869563 (77.7%) missing valuesMissing
관리비 업로드 has 869563 (77.7%) missing valuesMissing
좌표X has 869670 (77.7%) missing valuesMissing
좌표Y has 869670 (77.7%) missing valuesMissing
단지신청일 has 869625 (77.7%) missing valuesMissing
부번 is highly skewed (γ1 = 28.14286165)Skewed
k-전화번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
k-팩스번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부번 has 819792 (73.3%) zerosZeros
k-전용면적별세대현황(60㎡이하) has 53781 (4.8%) zerosZeros
k-전용면적별세대현황(60㎡~85㎡이하) has 42969 (3.8%) zerosZeros
k-85㎡~135㎡이하 has 99459 (8.9%) zerosZeros
건축면적 has 117399 (10.5%) zerosZeros
주차대수 has 17945 (1.6%) zerosZeros

Reproduction

Analysis started2024-07-10 07:51:35.843773
Analysis finished2024-07-10 07:55:04.883463
Duration3 minutes and 29.04 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct339
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:05.005542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.201177
Min length11

Characters and Unicode

Total characters14769767
Distinct characters197
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row서울특별시 강남구 개포동
2nd row서울특별시 강남구 개포동
3rd row서울특별시 강남구 개포동
4th row서울특별시 강남구 개포동
5th row서울특별시 강남구 개포동
ValueCountFrequency (%)
서울특별시 1118822
33.3%
노원구 115099
 
3.4%
송파구 73785
 
2.2%
강남구 69083
 
2.1%
강서구 66610
 
2.0%
강동구 61895
 
1.8%
구로구 57604
 
1.7%
성북구 56675
 
1.7%
양천구 53977
 
1.6%
서초구 53126
 
1.6%
Other values (353) 1629790
48.6%
2024-07-10T16:55:05.192212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2237644
15.2%
1298349
 
8.8%
1293917
 
8.8%
1210931
 
8.2%
1130106
 
7.7%
1118822
 
7.6%
1118822
 
7.6%
1118822
 
7.6%
225255
 
1.5%
136124
 
0.9%
Other values (187) 3880975
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14769767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2237644
15.2%
1298349
 
8.8%
1293917
 
8.8%
1210931
 
8.2%
1130106
 
7.7%
1118822
 
7.6%
1118822
 
7.6%
1118822
 
7.6%
225255
 
1.5%
136124
 
0.9%
Other values (187) 3880975
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14769767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2237644
15.2%
1298349
 
8.8%
1293917
 
8.8%
1210931
 
8.2%
1130106
 
7.7%
1118822
 
7.6%
1118822
 
7.6%
1118822
 
7.6%
225255
 
1.5%
136124
 
0.9%
Other values (187) 3880975
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14769767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2237644
15.2%
1298349
 
8.8%
1293917
 
8.8%
1210931
 
8.2%
1130106
 
7.7%
1118822
 
7.6%
1118822
 
7.6%
1118822
 
7.6%
225255
 
1.5%
136124
 
0.9%
Other values (187) 3880975
26.3%

번지
Text

Distinct6572
Distinct (%)0.6%
Missing225
Missing (%)< 0.1%
Memory size8.5 MiB
2024-07-10T16:55:05.339742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.6077667
Min length1

Characters and Unicode

Total characters4035637
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)< 0.1%

Sample

1st row658-1
2nd row658-1
3rd row658-1
4th row658-1
5th row658-1
ValueCountFrequency (%)
13 4629
 
0.4%
17 4548
 
0.4%
10 4531
 
0.4%
347 4264
 
0.4%
105 4158
 
0.4%
481 4144
 
0.4%
647 4049
 
0.4%
22 4007
 
0.4%
1353 3997
 
0.4%
12 3914
 
0.3%
Other values (6562) 1076356
96.2%
2024-07-10T16:55:05.546039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 717774
17.8%
2 401665
10.0%
3 391787
9.7%
4 377678
9.4%
5 364582
9.0%
7 339029
8.4%
6 326613
8.1%
0 309891
7.7%
- 298966
7.4%
8 260343
 
6.5%
Other values (3) 247309
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4035637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 717774
17.8%
2 401665
10.0%
3 391787
9.7%
4 377678
9.4%
5 364582
9.0%
7 339029
8.4%
6 326613
8.1%
0 309891
7.7%
- 298966
7.4%
8 260343
 
6.5%
Other values (3) 247309
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4035637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 717774
17.8%
2 401665
10.0%
3 391787
9.7%
4 377678
9.4%
5 364582
9.0%
7 339029
8.4%
6 326613
8.1%
0 309891
7.7%
- 298966
7.4%
8 260343
 
6.5%
Other values (3) 247309
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4035637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 717774
17.8%
2 401665
10.0%
3 391787
9.7%
4 377678
9.4%
5 364582
9.0%
7 339029
8.4%
6 326613
8.1%
0 309891
7.7%
- 298966
7.4%
8 260343
 
6.5%
Other values (3) 247309
 
6.1%

본번
Real number (ℝ)

Distinct1523
Distinct (%)0.1%
Missing75
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean564.91082
Minimum0
Maximum4974
Zeros150
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:05.624099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q1176
median470
Q3781
95-th percentile1475
Maximum4974
Range4974
Interquartile range (IQR)605

Descriptive statistics

Standard deviation516.0642
Coefficient of variation (CV)0.91353216
Kurtosis16.541624
Mean564.91082
Median Absolute Deviation (MAD)299
Skewness2.700316
Sum6.3199229 × 108
Variance266322.26
MonotonicityNot monotonic
2024-07-10T16:55:05.681125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 7035
 
0.6%
17 5485
 
0.5%
13 5238
 
0.5%
271 4983
 
0.4%
1 4947
 
0.4%
19 4916
 
0.4%
15 4863
 
0.4%
20 4863
 
0.4%
10 4816
 
0.4%
22 4547
 
0.4%
Other values (1513) 1067054
95.4%
ValueCountFrequency (%)
0 150
 
< 0.1%
1 4947
0.4%
2 2874
0.3%
3 1005
 
0.1%
4 1691
 
0.2%
5 1331
 
0.1%
6 1833
 
0.2%
7 2270
0.2%
8 468
 
< 0.1%
9 1409
 
0.1%
ValueCountFrequency (%)
4974 1
 
< 0.1%
4969 29
 
< 0.1%
4967 10
 
< 0.1%
4964 53
 
< 0.1%
4958 57
 
< 0.1%
4955 50
 
< 0.1%
4950 305
< 0.1%
4945 264
< 0.1%
4944 25
 
< 0.1%
4943 115
 
< 0.1%

부번
Real number (ℝ)

SKEWED  ZEROS 

Distinct329
Distinct (%)< 0.1%
Missing75
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.9788853
Minimum0
Maximum2837
Zeros819792
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:05.734282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile21
Maximum2837
Range2837
Interquartile range (IQR)1

Descriptive statistics

Standard deviation46.685836
Coefficient of variation (CV)7.8084516
Kurtosis1147.9327
Mean5.9788853
Median Absolute Deviation (MAD)0
Skewness28.142862
Sum6688860
Variance2179.5673
MonotonicityNot monotonic
2024-07-10T16:55:05.788164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 819792
73.3%
1 94949
 
8.5%
2 27815
 
2.5%
3 17983
 
1.6%
5 13946
 
1.2%
4 13811
 
1.2%
8 8385
 
0.7%
7 7706
 
0.7%
6 7304
 
0.7%
9 6166
 
0.6%
Other values (319) 100890
 
9.0%
ValueCountFrequency (%)
0 819792
73.3%
1 94949
 
8.5%
2 27815
 
2.5%
3 17983
 
1.6%
4 13811
 
1.2%
5 13946
 
1.2%
6 7304
 
0.7%
7 7706
 
0.7%
8 8385
 
0.7%
9 6166
 
0.6%
ValueCountFrequency (%)
2837 23
< 0.1%
2745 13
< 0.1%
2164 13
< 0.1%
2066 14
< 0.1%
2013 6
 
< 0.1%
2008 7
 
< 0.1%
2006 15
< 0.1%
2001 5
 
< 0.1%
2000 19
< 0.1%
1982 15
< 0.1%
Distinct6538
Distinct (%)0.6%
Missing2126
Missing (%)0.2%
Memory size8.5 MiB
2024-07-10T16:55:05.915873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.2485905
Min length1

Characters and Unicode

Total characters5861080
Distinct characters624
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)< 0.1%

Sample

1st row개포6차우성
2nd row개포6차우성
3rd row개포6차우성
4th row개포6차우성
5th row개포6차우성
ValueCountFrequency (%)
현대 16644
 
1.5%
신동아 12924
 
1.1%
한신 10268
 
0.9%
두산 8533
 
0.8%
주공2 7918
 
0.7%
우성 7768
 
0.7%
벽산 7285
 
0.6%
삼성래미안 7143
 
0.6%
대림 6305
 
0.6%
극동 5669
 
0.5%
Other values (6545) 1043002
92.0%
2024-07-10T16:55:06.116856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164223
 
2.8%
151596
 
2.6%
143395
 
2.4%
1 134958
 
2.3%
126202
 
2.2%
118596
 
2.0%
115032
 
2.0%
114368
 
2.0%
112302
 
1.9%
2 102860
 
1.8%
Other values (614) 4577548
78.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5861080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
164223
 
2.8%
151596
 
2.6%
143395
 
2.4%
1 134958
 
2.3%
126202
 
2.2%
118596
 
2.0%
115032
 
2.0%
114368
 
2.0%
112302
 
1.9%
2 102860
 
1.8%
Other values (614) 4577548
78.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5861080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
164223
 
2.8%
151596
 
2.6%
143395
 
2.4%
1 134958
 
2.3%
126202
 
2.2%
118596
 
2.0%
115032
 
2.0%
114368
 
2.0%
112302
 
1.9%
2 102860
 
1.8%
Other values (614) 4577548
78.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5861080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
164223
 
2.8%
151596
 
2.6%
143395
 
2.4%
1 134958
 
2.3%
126202
 
2.2%
118596
 
2.0%
115032
 
2.0%
114368
 
2.0%
112302
 
1.9%
2 102860
 
1.8%
Other values (614) 4577548
78.1%

전용면적(㎡)
Real number (ℝ)

Distinct14617
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.174747
Minimum10.02
Maximum424.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:06.187904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10.02
5-th percentile38.52
Q159.65
median81.88
Q384.96
95-th percentile131.4
Maximum424.32
Range414.3
Interquartile range (IQR)25.31

Descriptive statistics

Standard deviation29.364231
Coefficient of variation (CV)0.38049015
Kurtosis3.835133
Mean77.174747
Median Absolute Deviation (MAD)21.92
Skewness1.2521309
Sum86344805
Variance862.25808
MonotonicityNot monotonic
2024-07-10T16:55:06.243254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.96 20420
 
1.8%
84.97 19892
 
1.8%
84.99 19285
 
1.7%
84.98 19010
 
1.7%
59.94 13864
 
1.2%
59.99 12334
 
1.1%
84.9 11975
 
1.1%
84.95 11645
 
1.0%
59.96 11348
 
1.0%
84.94 11158
 
1.0%
Other values (14607) 967891
86.5%
ValueCountFrequency (%)
10.02 1
 
< 0.1%
10.156 11
< 0.1%
10.288 11
< 0.1%
10.3215 2
 
< 0.1%
10.78 9
< 0.1%
11.33 3
 
< 0.1%
11.48 1
 
< 0.1%
11.6448 2
 
< 0.1%
11.6657 3
 
< 0.1%
11.79 7
< 0.1%
ValueCountFrequency (%)
424.32 1
 
< 0.1%
395.06 1
 
< 0.1%
325.39 1
 
< 0.1%
317.36 6
< 0.1%
309.7 1
 
< 0.1%
301.47 1
 
< 0.1%
283.76 2
 
< 0.1%
283 1
 
< 0.1%
273.967 2
 
< 0.1%
273.96 4
< 0.1%

계약년월
Real number (ℝ)

Distinct198
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201476.04
Minimum200701
Maximum202306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:06.296778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum200701
5-th percentile200712
Q1201110
median201507
Q3201804
95-th percentile202104
Maximum202306
Range1605
Interquartile range (IQR)694

Descriptive statistics

Standard deviation418.78677
Coefficient of variation (CV)0.0020785934
Kurtosis-0.89762611
Mean201476.04
Median Absolute Deviation (MAD)301
Skewness-0.21549085
Sum2.2541582 × 1011
Variance175382.36
MonotonicityNot monotonic
2024-07-10T16:55:06.393296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202006 16364
 
1.5%
201503 15510
 
1.4%
201808 14990
 
1.3%
201707 14922
 
1.3%
201705 14766
 
1.3%
201606 13299
 
1.2%
201706 12843
 
1.1%
201801 12571
 
1.1%
201610 12121
 
1.1%
201607 11836
 
1.1%
Other values (188) 979600
87.6%
ValueCountFrequency (%)
200701 4350
0.4%
200702 3749
0.3%
200703 5449
0.5%
200704 4442
0.4%
200705 3607
0.3%
200706 4980
0.4%
200707 5735
0.5%
200708 4877
0.4%
200709 4444
0.4%
200710 6528
0.6%
ValueCountFrequency (%)
202306 3867
0.3%
202305 3500
0.3%
202304 3265
0.3%
202303 2889
0.3%
202302 2539
0.2%
202301 1488
 
0.1%
202212 855
 
0.1%
202211 646
 
0.1%
202210 576
 
0.1%
202209 620
 
0.1%

계약일
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
20
 
40053
10
 
39165
15
 
39100
13
 
38198
18
 
37938
Other values (26)
924368 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2237644
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08
2nd row22
3rd row28
4th row03
5th row08

Common Values

ValueCountFrequency (%)
20 40053
 
3.6%
10 39165
 
3.5%
15 39100
 
3.5%
13 38198
 
3.4%
18 37938
 
3.4%
16 37816
 
3.4%
11 37806
 
3.4%
23 37412
 
3.3%
25 37318
 
3.3%
27 37184
 
3.3%
Other values (21) 736832
65.9%

Length

2024-07-10T16:55:06.447403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20 40053
 
3.6%
10 39165
 
3.5%
15 39100
 
3.5%
13 38198
 
3.4%
18 37938
 
3.4%
16 37816
 
3.4%
11 37806
 
3.4%
23 37412
 
3.3%
25 37318
 
3.3%
27 37184
 
3.3%
Other values (21) 736832
65.9%

Most occurring characters

ValueCountFrequency (%)
1 506371
22.6%
2 473920
21.2%
0 435288
19.5%
3 165814
 
7.4%
5 111839
 
5.0%
8 110204
 
4.9%
7 110184
 
4.9%
6 108737
 
4.9%
4 108349
 
4.8%
9 106938
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2237644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 506371
22.6%
2 473920
21.2%
0 435288
19.5%
3 165814
 
7.4%
5 111839
 
5.0%
8 110204
 
4.9%
7 110184
 
4.9%
6 108737
 
4.9%
4 108349
 
4.8%
9 106938
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2237644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 506371
22.6%
2 473920
21.2%
0 435288
19.5%
3 165814
 
7.4%
5 111839
 
5.0%
8 110204
 
4.9%
7 110184
 
4.9%
6 108737
 
4.9%
4 108349
 
4.8%
9 106938
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2237644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 506371
22.6%
2 473920
21.2%
0 435288
19.5%
3 165814
 
7.4%
5 111839
 
5.0%
8 110204
 
4.9%
7 110184
 
4.9%
6 108737
 
4.9%
4 108349
 
4.8%
9 106938
 
4.8%


Real number (ℝ)

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.871968
Minimum-4
Maximum69
Zeros0
Zeros (%)0.0%
Negative261
Negative (%)< 0.1%
Memory size8.5 MiB
2024-07-10T16:55:06.497692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile1
Q14
median8
Q312
95-th percentile20
Maximum69
Range73
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.9825845
Coefficient of variation (CV)0.6743244
Kurtosis2.7267716
Mean8.871968
Median Absolute Deviation (MAD)4
Skewness1.1491684
Sum9926153
Variance35.791317
MonotonicityNot monotonic
2024-07-10T16:55:06.555495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 82188
 
7.3%
4 82045
 
7.3%
3 80561
 
7.2%
2 77037
 
6.9%
6 73616
 
6.6%
7 70303
 
6.3%
1 67128
 
6.0%
8 66198
 
5.9%
9 64358
 
5.8%
10 62541
 
5.6%
Other values (63) 392847
35.1%
ValueCountFrequency (%)
-4 4
 
< 0.1%
-3 10
 
< 0.1%
-2 31
 
< 0.1%
-1 216
 
< 0.1%
1 67128
6.0%
2 77037
6.9%
3 80561
7.2%
4 82045
7.3%
5 82188
7.3%
6 73616
6.6%
ValueCountFrequency (%)
69 2
 
< 0.1%
68 6
< 0.1%
67 6
< 0.1%
66 11
< 0.1%
65 5
< 0.1%
64 11
< 0.1%
63 12
< 0.1%
62 9
< 0.1%
61 8
< 0.1%
60 9
< 0.1%

건축년도
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.7553
Minimum1961
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:06.612968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1961
5-th percentile1982
Q11992
median2000
Q32005
95-th percentile2013
Maximum2023
Range62
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3339081
Coefficient of variation (CV)0.0046698603
Kurtosis-0.1391388
Mean1998.7553
Median Absolute Deviation (MAD)6
Skewness-0.34328075
Sum2.2362514 × 109
Variance87.121841
MonotonicityNot monotonic
2024-07-10T16:55:06.670512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2004 68487
 
6.1%
2003 65601
 
5.9%
1999 59802
 
5.3%
2000 56851
 
5.1%
1988 55581
 
5.0%
2005 48372
 
4.3%
1998 44049
 
3.9%
2001 42293
 
3.8%
1997 42227
 
3.8%
2002 41929
 
3.7%
Other values (50) 593630
53.1%
ValueCountFrequency (%)
1961 54
 
< 0.1%
1965 17
 
< 0.1%
1966 105
 
< 0.1%
1967 17
 
< 0.1%
1968 251
 
< 0.1%
1969 536
 
< 0.1%
1970 688
 
0.1%
1971 2396
0.2%
1972 679
 
0.1%
1973 1046
0.1%
ValueCountFrequency (%)
2023 79
 
< 0.1%
2022 673
 
0.1%
2021 1707
 
0.2%
2020 2773
 
0.2%
2019 4802
 
0.4%
2018 4858
 
0.4%
2017 4671
 
0.4%
2016 8364
0.7%
2015 8800
0.8%
2014 17199
1.5%
Distinct9232
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:06.776751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length8.4242417
Min length1

Characters and Unicode

Total characters9425227
Distinct characters299
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)< 0.1%

Sample

1st row언주로 3
2nd row언주로 3
3rd row언주로 3
4th row언주로 3
5th row언주로 3
ValueCountFrequency (%)
17 16226
 
0.7%
8 15627
 
0.7%
16 15501
 
0.7%
28 15301
 
0.7%
15 15071
 
0.7%
19 15040
 
0.7%
21 14355
 
0.6%
20 13883
 
0.6%
10 13733
 
0.6%
30 13511
 
0.6%
Other values (5476) 2085581
93.4%
2024-07-10T16:55:06.951583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1118821
 
11.9%
1095586
 
11.6%
1 746077
 
7.9%
577750
 
6.1%
2 540310
 
5.7%
3 427191
 
4.5%
4 348810
 
3.7%
5 348198
 
3.7%
0 301510
 
3.2%
6 292838
 
3.1%
Other values (289) 3628136
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9425227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1118821
 
11.9%
1095586
 
11.6%
1 746077
 
7.9%
577750
 
6.1%
2 540310
 
5.7%
3 427191
 
4.5%
4 348810
 
3.7%
5 348198
 
3.7%
0 301510
 
3.2%
6 292838
 
3.1%
Other values (289) 3628136
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9425227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1118821
 
11.9%
1095586
 
11.6%
1 746077
 
7.9%
577750
 
6.1%
2 540310
 
5.7%
3 427191
 
4.5%
4 348810
 
3.7%
5 348198
 
3.7%
0 301510
 
3.2%
6 292838
 
3.1%
Other values (289) 3628136
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9425227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1118821
 
11.9%
1095586
 
11.6%
1 746077
 
7.9%
577750
 
6.1%
2 540310
 
5.7%
3 427191
 
4.5%
4 348810
 
3.7%
5 348198
 
3.7%
0 301510
 
3.2%
6 292838
 
3.1%
Other values (289) 3628136
38.5%

해제사유발생일
Real number (ℝ)

MISSING 

Distinct1025
Distinct (%)17.1%
Missing1112839
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean20210570
Minimum20200221
Maximum20230926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:07.024280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20200221
5-th percentile20200515
Q120200820
median20210304
Q320220211
95-th percentile20230614
Maximum20230926
Range30705
Interquartile range (IQR)19391

Descriptive statistics

Standard deviation10606.969
Coefficient of variation (CV)0.00052482283
Kurtosis-0.82772259
Mean20210570
Median Absolute Deviation (MAD)9573
Skewness0.70669853
Sum1.2091984 × 1011
Variance1.1250778 × 108
MonotonicityNot monotonic
2024-07-10T16:55:07.084187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20220211 151
 
< 0.1%
20200727 43
 
< 0.1%
20200714 40
 
< 0.1%
20200720 37
 
< 0.1%
20200715 32
 
< 0.1%
20200713 31
 
< 0.1%
20200708 30
 
< 0.1%
20210326 28
 
< 0.1%
20200706 28
 
< 0.1%
20200629 27
 
< 0.1%
Other values (1015) 5536
 
0.5%
(Missing) 1112839
99.5%
ValueCountFrequency (%)
20200221 2
 
< 0.1%
20200224 2
 
< 0.1%
20200225 2
 
< 0.1%
20200227 2
 
< 0.1%
20200302 4
< 0.1%
20200303 1
 
< 0.1%
20200304 2
 
< 0.1%
20200305 2
 
< 0.1%
20200306 9
< 0.1%
20200309 1
 
< 0.1%
ValueCountFrequency (%)
20230926 1
 
< 0.1%
20230925 1
 
< 0.1%
20230922 2
< 0.1%
20230921 2
< 0.1%
20230920 4
< 0.1%
20230919 2
< 0.1%
20230915 1
 
< 0.1%
20230914 1
 
< 0.1%
20230913 2
< 0.1%
20230912 1
 
< 0.1%
Distinct182
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:07.183919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.0933169
Min length1

Characters and Unicode

Total characters1223227
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
20230630 585
 
3.9%
20230428 394
 
2.6%
20230831 391
 
2.6%
20230530 367
 
2.5%
20230731 352
 
2.4%
20230728 335
 
2.2%
20230531 305
 
2.0%
20230830 240
 
1.6%
20230526 231
 
1.5%
20230331 200
 
1.3%
Other values (171) 11515
77.2%
2024-07-10T16:55:07.352628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1103907
90.2%
2 36345
 
3.0%
0 35406
 
2.9%
3 19576
 
1.6%
1 6757
 
0.6%
8 4298
 
0.4%
7 4101
 
0.3%
5 4010
 
0.3%
6 3710
 
0.3%
4 3033
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1223227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1103907
90.2%
2 36345
 
3.0%
0 35406
 
2.9%
3 19576
 
1.6%
1 6757
 
0.6%
8 4298
 
0.4%
7 4101
 
0.3%
5 4010
 
0.3%
6 3710
 
0.3%
4 3033
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1223227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1103907
90.2%
2 36345
 
3.0%
0 35406
 
2.9%
3 19576
 
1.6%
1 6757
 
0.6%
8 4298
 
0.4%
7 4101
 
0.3%
5 4010
 
0.3%
6 3710
 
0.3%
4 3033
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1223227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1103907
90.2%
2 36345
 
3.0%
0 35406
 
2.9%
3 19576
 
1.6%
1 6757
 
0.6%
8 4298
 
0.4%
7 4101
 
0.3%
5 4010
 
0.3%
6 3710
 
0.3%
4 3033
 
0.2%

거래유형
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
-
1086451 
중개거래
 
29271
직거래
 
3100

Length

Max length4
Median length1
Mean length1.0840286
Min length1

Characters and Unicode

Total characters1212835
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 1086451
97.1%
중개거래 29271
 
2.6%
직거래 3100
 
0.3%

Length

2024-07-10T16:55:07.429418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:07.478991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1086451
97.1%
중개거래 29271
 
2.6%
직거래 3100
 
0.3%

Most occurring characters

ValueCountFrequency (%)
- 1086451
89.6%
32371
 
2.7%
32371
 
2.7%
29271
 
2.4%
29271
 
2.4%
3100
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1212835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1086451
89.6%
32371
 
2.7%
32371
 
2.7%
29271
 
2.4%
29271
 
2.4%
3100
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1212835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1086451
89.6%
32371
 
2.7%
32371
 
2.7%
29271
 
2.4%
29271
 
2.4%
3100
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1212835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1086451
89.6%
32371
 
2.7%
32371
 
2.7%
29271
 
2.4%
29271
 
2.4%
3100
 
0.3%
Distinct643
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:07.533739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length24
Median length1
Mean length1.1523495
Min length1

Characters and Unicode

Total characters1289274
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)< 0.1%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
1089581
94.5%
서울 31031
 
2.7%
강남구 2413
 
0.2%
송파구 2363
 
0.2%
노원구 2199
 
0.2%
강동구 1797
 
0.2%
서초구 1658
 
0.1%
강서구 1547
 
0.1%
성북구 1531
 
0.1%
영등포구 1515
 
0.1%
Other values (110) 17455
 
1.5%
2024-07-10T16:55:07.656929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1089581
84.5%
35324
 
2.7%
34268
 
2.7%
32834
 
2.5%
31034
 
2.4%
6296
 
0.5%
5591
 
0.4%
2934
 
0.2%
2892
 
0.2%
2674
 
0.2%
Other values (92) 45846
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1289274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1089581
84.5%
35324
 
2.7%
34268
 
2.7%
32834
 
2.5%
31034
 
2.4%
6296
 
0.5%
5591
 
0.4%
2934
 
0.2%
2892
 
0.2%
2674
 
0.2%
Other values (92) 45846
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1289274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1089581
84.5%
35324
 
2.7%
34268
 
2.7%
32834
 
2.5%
31034
 
2.4%
6296
 
0.5%
5591
 
0.4%
2934
 
0.2%
2892
 
0.2%
2674
 
0.2%
Other values (92) 45846
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1289274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1089581
84.5%
35324
 
2.7%
34268
 
2.7%
32834
 
2.5%
31034
 
2.4%
6296
 
0.5%
5591
 
0.4%
2934
 
0.2%
2892
 
0.2%
2674
 
0.2%
Other values (92) 45846
 
3.6%

k-단지분류(아파트,주상복합등등)
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing870691
Missing (%)77.8%
Memory size8.5 MiB
아파트
235994 
주상복합
 
11360
도시형 생활주택(주상복합)
 
500
도시형 생활주택(아파트)
 
152
연립주택
 
125

Length

Max length14
Median length3
Mean length3.0745775
Min length3

Characters and Unicode

Total characters762898
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아파트
2nd row아파트
3rd row아파트
4th row아파트
5th row아파트

Common Values

ValueCountFrequency (%)
아파트 235994
 
21.1%
주상복합 11360
 
1.0%
도시형 생활주택(주상복합) 500
 
< 0.1%
도시형 생활주택(아파트) 152
 
< 0.1%
연립주택 125
 
< 0.1%
(Missing) 870691
77.8%

Length

2024-07-10T16:55:07.719692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:07.761687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
아파트 235994
94.9%
주상복합 11360
 
4.6%
도시형 652
 
0.3%
생활주택(주상복합 500
 
0.2%
생활주택(아파트 152
 
0.1%
연립주택 125
 
0.1%

Most occurring characters

ValueCountFrequency (%)
236146
31.0%
236146
31.0%
236146
31.0%
12637
 
1.7%
11860
 
1.6%
11860
 
1.6%
11860
 
1.6%
777
 
0.1%
652
 
0.1%
) 652
 
0.1%
Other values (8) 4162
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 762898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
236146
31.0%
236146
31.0%
236146
31.0%
12637
 
1.7%
11860
 
1.6%
11860
 
1.6%
11860
 
1.6%
777
 
0.1%
652
 
0.1%
) 652
 
0.1%
Other values (8) 4162
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 762898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
236146
31.0%
236146
31.0%
236146
31.0%
12637
 
1.7%
11860
 
1.6%
11860
 
1.6%
11860
 
1.6%
777
 
0.1%
652
 
0.1%
) 652
 
0.1%
Other values (8) 4162
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 762898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
236146
31.0%
236146
31.0%
236146
31.0%
12637
 
1.7%
11860
 
1.6%
11860
 
1.6%
11860
 
1.6%
777
 
0.1%
652
 
0.1%
) 652
 
0.1%
Other values (8) 4162
 
0.5%

k-전화번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing870274
Missing (%)77.8%
Memory size8.5 MiB

k-팩스번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing872742
Missing (%)78.0%
Memory size8.5 MiB

단지소개기존clob
Real number (ℝ)

MISSING 

Distinct94
Distinct (%)0.1%
Missing1050240
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean541.52998
Minimum1
Maximum2888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:07.813306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median174
Q3725
95-th percentile2269
Maximum2888
Range2887
Interquartile range (IQR)721

Descriptive statistics

Standard deviation751.80985
Coefficient of variation (CV)1.388307
Kurtosis0.50781073
Mean541.52998
Median Absolute Deviation (MAD)170
Skewness1.3769033
Sum37139209
Variance565218.05
MonotonicityNot monotonic
2024-07-10T16:55:07.870059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 23845
 
2.1%
1474 2465
 
0.2%
638 2346
 
0.2%
301 2093
 
0.2%
2315 1558
 
0.1%
297 1496
 
0.1%
144 1490
 
0.1%
1269 1053
 
0.1%
1208 1035
 
0.1%
2162 1034
 
0.1%
Other values (84) 30167
 
2.7%
(Missing) 1050240
93.9%
ValueCountFrequency (%)
1 128
 
< 0.1%
4 23845
2.1%
5 372
 
< 0.1%
8 196
 
< 0.1%
9 122
 
< 0.1%
19 226
 
< 0.1%
23 675
 
0.1%
24 107
 
< 0.1%
28 510
 
< 0.1%
40 245
 
< 0.1%
ValueCountFrequency (%)
2888 159
 
< 0.1%
2480 145
 
< 0.1%
2351 628
0.1%
2315 1558
0.1%
2304 582
 
0.1%
2286 121
 
< 0.1%
2269 292
 
< 0.1%
2262 553
 
< 0.1%
2196 246
 
< 0.1%
2168 468
 
< 0.1%

k-세대타입(분양형태)
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing869563
Missing (%)77.7%
Memory size8.5 MiB
분양
206371 
기타
40686 
임대
 
2202

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters498518
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row분양
2nd row분양
3rd row분양
4th row분양
5th row분양

Common Values

ValueCountFrequency (%)
분양 206371
 
18.4%
기타 40686
 
3.6%
임대 2202
 
0.2%
(Missing) 869563
77.7%

Length

2024-07-10T16:55:07.921048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:07.959444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
분양 206371
82.8%
기타 40686
 
16.3%
임대 2202
 
0.9%

Most occurring characters

ValueCountFrequency (%)
206371
41.4%
206371
41.4%
40686
 
8.2%
40686
 
8.2%
2202
 
0.4%
2202
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 498518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
206371
41.4%
206371
41.4%
40686
 
8.2%
40686
 
8.2%
2202
 
0.4%
2202
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 498518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
206371
41.4%
206371
41.4%
40686
 
8.2%
40686
 
8.2%
2202
 
0.4%
2202
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 498518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
206371
41.4%
206371
41.4%
40686
 
8.2%
40686
 
8.2%
2202
 
0.4%
2202
 
0.4%

k-관리방식
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing869563
Missing (%)77.7%
Memory size8.5 MiB
위탁관리
227842 
자치관리
 
18005
직영
 
3412

Length

Max length4
Median length4
Mean length3.9726229
Min length2

Characters and Unicode

Total characters990212
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자치관리
2nd row자치관리
3rd row자치관리
4th row자치관리
5th row자치관리

Common Values

ValueCountFrequency (%)
위탁관리 227842
 
20.4%
자치관리 18005
 
1.6%
직영 3412
 
0.3%
(Missing) 869563
77.7%

Length

2024-07-10T16:55:08.006903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:08.049820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
위탁관리 227842
91.4%
자치관리 18005
 
7.2%
직영 3412
 
1.4%

Most occurring characters

ValueCountFrequency (%)
245847
24.8%
245847
24.8%
227842
23.0%
227842
23.0%
18005
 
1.8%
18005
 
1.8%
3412
 
0.3%
3412
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 990212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
245847
24.8%
245847
24.8%
227842
23.0%
227842
23.0%
18005
 
1.8%
18005
 
1.8%
3412
 
0.3%
3412
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 990212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
245847
24.8%
245847
24.8%
227842
23.0%
227842
23.0%
18005
 
1.8%
18005
 
1.8%
3412
 
0.3%
3412
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 990212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
245847
24.8%
245847
24.8%
227842
23.0%
227842
23.0%
18005
 
1.8%
18005
 
1.8%
3412
 
0.3%
3412
 
0.3%

k-복도유형
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing869890
Missing (%)77.8%
Memory size8.5 MiB
계단식
123651 
혼합식
84491 
복도식
38383 
타워형
 
1340
기타
 
1067

Length

Max length3
Median length3
Mean length2.9957137
Min length2

Characters and Unicode

Total characters745729
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row계단식
2nd row계단식
3rd row계단식
4th row계단식
5th row계단식

Common Values

ValueCountFrequency (%)
계단식 123651
 
11.1%
혼합식 84491
 
7.6%
복도식 38383
 
3.4%
타워형 1340
 
0.1%
기타 1067
 
0.1%
(Missing) 869890
77.8%

Length

2024-07-10T16:55:08.092982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:08.135504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
계단식 123651
49.7%
혼합식 84491
33.9%
복도식 38383
 
15.4%
타워형 1340
 
0.5%
기타 1067
 
0.4%

Most occurring characters

ValueCountFrequency (%)
246525
33.1%
123651
16.6%
123651
16.6%
84491
 
11.3%
84491
 
11.3%
38383
 
5.1%
38383
 
5.1%
2407
 
0.3%
1340
 
0.2%
1340
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 745729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
246525
33.1%
123651
16.6%
123651
16.6%
84491
 
11.3%
84491
 
11.3%
38383
 
5.1%
38383
 
5.1%
2407
 
0.3%
1340
 
0.2%
1340
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 745729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
246525
33.1%
123651
16.6%
123651
16.6%
84491
 
11.3%
84491
 
11.3%
38383
 
5.1%
38383
 
5.1%
2407
 
0.3%
1340
 
0.2%
1340
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 745729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
246525
33.1%
123651
16.6%
123651
16.6%
84491
 
11.3%
84491
 
11.3%
38383
 
5.1%
38383
 
5.1%
2407
 
0.3%
1340
 
0.2%
1340
 
0.2%

k-난방방식
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing869563
Missing (%)77.7%
Memory size8.5 MiB
개별난방
150739 
지역난방
83891 
중앙난방
 
12591
기타
 
2038

Length

Max length4
Median length4
Mean length3.9836475
Min length2

Characters and Unicode

Total characters992960
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개별난방
2nd row개별난방
3rd row개별난방
4th row개별난방
5th row개별난방

Common Values

ValueCountFrequency (%)
개별난방 150739
 
13.5%
지역난방 83891
 
7.5%
중앙난방 12591
 
1.1%
기타 2038
 
0.2%
(Missing) 869563
77.7%

Length

2024-07-10T16:55:08.188894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:08.232644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
개별난방 150739
60.5%
지역난방 83891
33.7%
중앙난방 12591
 
5.1%
기타 2038
 
0.8%

Most occurring characters

ValueCountFrequency (%)
247221
24.9%
247221
24.9%
150739
15.2%
150739
15.2%
83891
 
8.4%
83891
 
8.4%
12591
 
1.3%
12591
 
1.3%
2038
 
0.2%
2038
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 992960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
247221
24.9%
247221
24.9%
150739
15.2%
150739
15.2%
83891
 
8.4%
83891
 
8.4%
12591
 
1.3%
12591
 
1.3%
2038
 
0.2%
2038
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 992960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
247221
24.9%
247221
24.9%
150739
15.2%
150739
15.2%
83891
 
8.4%
83891
 
8.4%
12591
 
1.3%
12591
 
1.3%
2038
 
0.2%
2038
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 992960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
247221
24.9%
247221
24.9%
150739
15.2%
150739
15.2%
83891
 
8.4%
83891
 
8.4%
12591
 
1.3%
12591
 
1.3%
2038
 
0.2%
2038
 
0.2%

k-전체동수
Real number (ℝ)

MISSING 

Distinct41
Distinct (%)< 0.1%
Missing870630
Missing (%)77.8%
Infinite0
Infinite (%)0.0%
Mean14.798346
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:08.280129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q317
95-th percentile44
Maximum124
Range123
Interquartile range (IQR)12

Descriptive statistics

Standard deviation17.693533
Coefficient of variation (CV)1.1956426
Kurtosis15.999616
Mean14.798346
Median Absolute Deviation (MAD)6
Skewness3.4634499
Sum3672831
Variance313.0611
MonotonicityNot monotonic
2024-07-10T16:55:08.335439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10 19801
 
1.8%
2 17162
 
1.5%
6 16167
 
1.4%
3 15271
 
1.4%
4 13245
 
1.2%
1 12348
 
1.1%
5 11775
 
1.1%
7 11694
 
1.0%
9 10825
 
1.0%
8 10644
 
1.0%
Other values (31) 109260
 
9.8%
(Missing) 870630
77.8%
ValueCountFrequency (%)
1 12348
1.1%
2 17162
1.5%
3 15271
1.4%
4 13245
1.2%
5 11775
1.1%
6 16167
1.4%
7 11694
1.0%
8 10644
1.0%
9 10825
1.0%
10 19801
1.8%
ValueCountFrequency (%)
124 2816
0.3%
84 484
 
< 0.1%
72 3028
0.3%
56 2589
0.2%
51 1490
0.1%
50 1365
0.1%
44 2346
0.2%
41 507
 
< 0.1%
40 1496
0.1%
37 1008
 
0.1%

k-전체세대수
Real number (ℝ)

MISSING 

Distinct521
Distinct (%)0.2%
Missing869563
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean1184.1246
Minimum59
Maximum9510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:08.392048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile194
Q1403
median768
Q31622
95-th percentile3710
Maximum9510
Range9451
Interquartile range (IQR)1219

Descriptive statistics

Standard deviation1191.4747
Coefficient of variation (CV)1.0062072
Kurtosis7.1513458
Mean1184.1246
Median Absolute Deviation (MAD)464
Skewness2.2978722
Sum2.9515371 × 108
Variance1419611.9
MonotonicityNot monotonic
2024-07-10T16:55:08.446263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5678 3028
 
0.3%
5040 2816
 
0.3%
4494 2589
 
0.2%
4424 2562
 
0.2%
2433 2465
 
0.2%
3710 2360
 
0.2%
3410 2346
 
0.2%
1696 2335
 
0.2%
1971 2278
 
0.2%
3002 2187
 
0.2%
Other values (511) 224293
 
20.0%
(Missing) 869563
77.7%
ValueCountFrequency (%)
59 36
 
< 0.1%
74 60
 
< 0.1%
77 26
 
< 0.1%
83 73
 
< 0.1%
86 49
 
< 0.1%
108 46
 
< 0.1%
111 89
 
< 0.1%
114 260
< 0.1%
115 101
 
< 0.1%
117 81
 
< 0.1%
ValueCountFrequency (%)
9510 484
 
< 0.1%
5678 3028
0.3%
5040 2816
0.3%
4494 2589
0.2%
4424 2562
0.2%
3710 2360
0.2%
3410 2346
0.2%
3293 1490
0.1%
3002 2187
0.2%
2856 2112
0.2%
Distinct344
Distinct (%)0.1%
Missing871058
Missing (%)77.9%
Memory size8.5 MiB
2024-07-10T16:55:08.566943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length17
Mean length6.0200634
Min length2

Characters and Unicode

Total characters1491555
Distinct characters203
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row우성건설
2nd row우성건설
3rd row우성건설
4th row우성건설
5th row우성건설
ValueCountFrequency (%)
삼성물산 15895
 
6.0%
대우건설 13814
 
5.2%
현대건설 11037
 
4.2%
현대산업개발 7882
 
3.0%
대한주택공사 7643
 
2.9%
gs건설 7384
 
2.8%
두산건설 6773
 
2.6%
삼성물산(주 4941
 
1.9%
현대건설(주 4711
 
1.8%
대림산업 3805
 
1.4%
Other values (324) 181342
68.4%
2024-07-10T16:55:08.748643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
149717
 
10.0%
142963
 
9.6%
86162
 
5.8%
85865
 
5.8%
64416
 
4.3%
) 64079
 
4.3%
( 63971
 
4.3%
, 45336
 
3.0%
44003
 
3.0%
43828
 
2.9%
Other values (193) 701215
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1491555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
149717
 
10.0%
142963
 
9.6%
86162
 
5.8%
85865
 
5.8%
64416
 
4.3%
) 64079
 
4.3%
( 63971
 
4.3%
, 45336
 
3.0%
44003
 
3.0%
43828
 
2.9%
Other values (193) 701215
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1491555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
149717
 
10.0%
142963
 
9.6%
86162
 
5.8%
85865
 
5.8%
64416
 
4.3%
) 64079
 
4.3%
( 63971
 
4.3%
, 45336
 
3.0%
44003
 
3.0%
43828
 
2.9%
Other values (193) 701215
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1491555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
149717
 
10.0%
142963
 
9.6%
86162
 
5.8%
85865
 
5.8%
64416
 
4.3%
) 64079
 
4.3%
( 63971
 
4.3%
, 45336
 
3.0%
44003
 
3.0%
43828
 
2.9%
Other values (193) 701215
47.0%

k-시행사
Text

MISSING 

Distinct555
Distinct (%)0.2%
Missing871254
Missing (%)77.9%
Memory size8.5 MiB
2024-07-10T16:55:08.854417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length26
Median length22
Mean length8.201787
Min length1

Characters and Unicode

Total characters2030500
Distinct characters339
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row모름
2nd row모름
3rd row모름
4th row모름
5th row모름
ValueCountFrequency (%)
sh공사 17140
 
5.6%
재건축조합 8258
 
2.7%
대한주택공사 7760
 
2.5%
재개발조합 6561
 
2.1%
조합 5849
 
1.9%
서울시 5632
 
1.8%
도시개발공사 5502
 
1.8%
sh 4999
 
1.6%
공사 4583
 
1.5%
현대건설 3832
 
1.2%
Other values (605) 237579
77.2%
2024-07-10T16:55:09.016640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
103798
 
5.1%
102226
 
5.0%
99582
 
4.9%
98255
 
4.8%
82459
 
4.1%
65272
 
3.2%
60707
 
3.0%
57845
 
2.8%
57526
 
2.8%
53649
 
2.6%
Other values (329) 1249181
61.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2030500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
103798
 
5.1%
102226
 
5.0%
99582
 
4.9%
98255
 
4.8%
82459
 
4.1%
65272
 
3.2%
60707
 
3.0%
57845
 
2.8%
57526
 
2.8%
53649
 
2.6%
Other values (329) 1249181
61.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2030500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
103798
 
5.1%
102226
 
5.0%
99582
 
4.9%
98255
 
4.8%
82459
 
4.1%
65272
 
3.2%
60707
 
3.0%
57845
 
2.8%
57526
 
2.8%
53649
 
2.6%
Other values (329) 1249181
61.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2030500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
103798
 
5.1%
102226
 
5.0%
99582
 
4.9%
98255
 
4.8%
82459
 
4.1%
65272
 
3.2%
60707
 
3.0%
57845
 
2.8%
57526
 
2.8%
53649
 
2.6%
Other values (329) 1249181
61.5%
Distinct673
Distinct (%)0.3%
Missing869696
Missing (%)77.7%
Memory size8.5 MiB
Minimum1976-07-09 00:00:00
Maximum2023-01-27 00:00:00
2024-07-10T16:55:09.084739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:55:09.144908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

k-연면적
Real number (ℝ)

MISSING 

Distinct734
Distinct (%)0.3%
Missing869563
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean161496.67
Minimum0
Maximum9591851
Zeros1067
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:09.208661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21770
Q153666
median101633
Q3203904
95-th percentile540103
Maximum9591851
Range9591851
Interquartile range (IQR)150238

Descriptive statistics

Standard deviation183985.6
Coefficient of variation (CV)1.1392532
Kurtosis281.3854
Mean161496.67
Median Absolute Deviation (MAD)58008
Skewness7.4166774
Sum4.0254499 × 1010
Variance3.3850703 × 1010
MonotonicityNot monotonic
2024-07-10T16:55:09.267924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
935380 3028
 
0.3%
240629 2816
 
0.3%
667132 2589
 
0.2%
504698 2562
 
0.2%
172371 2465
 
0.2%
280399 2360
 
0.2%
814368 2346
 
0.2%
280646 2278
 
0.2%
553661 2187
 
0.2%
307282 2118
 
0.2%
Other values (724) 224510
 
20.1%
(Missing) 869563
77.7%
ValueCountFrequency (%)
0 1067
0.1%
5817 147
 
< 0.1%
6418 134
 
< 0.1%
6581 62
 
< 0.1%
6997 29
 
< 0.1%
7294 90
 
< 0.1%
7354 82
 
< 0.1%
8140 95
 
< 0.1%
8353 134
 
< 0.1%
8502 36
 
< 0.1%
ValueCountFrequency (%)
9591851 10
 
< 0.1%
971190 484
 
< 0.1%
935380 3028
0.3%
814368 2346
0.2%
667132 2589
0.2%
575494 1490
0.1%
553661 2187
0.2%
540103 1439
0.1%
536020 516
 
< 0.1%
506394 1008
 
0.1%

k-주거전용면적
Real number (ℝ)

MISSING 

Distinct739
Distinct (%)0.3%
Missing869608
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean94210.105
Minimum2338
Maximum734781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:09.668554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2338
5-th percentile13480
Q131934
median60280
Q3117086
95-th percentile298679
Maximum734781
Range732443
Interquartile range (IQR)85152

Descriptive statistics

Standard deviation101905.96
Coefficient of variation (CV)1.0816882
Kurtosis7.7842399
Mean94210.105
Median Absolute Deviation (MAD)32933
Skewness2.5328542
Sum2.3478477 × 1010
Variance1.0384824 × 1010
MonotonicityNot monotonic
2024-07-10T16:55:09.726482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
470140 3028
 
0.3%
242461 2816
 
0.3%
537573 2589
 
0.2%
353089 2562
 
0.2%
117086 2465
 
0.2%
198027 2360
 
0.2%
405926 2346
 
0.2%
159726 2278
 
0.2%
298679 2187
 
0.2%
186888 2118
 
0.2%
Other values (729) 224465
 
20.1%
(Missing) 869608
77.7%
ValueCountFrequency (%)
2338 134
< 0.1%
2434 84
< 0.1%
2566 147
< 0.1%
2705 29
 
< 0.1%
2770 90
< 0.1%
2835 36
 
< 0.1%
2965 134
< 0.1%
3546 62
 
< 0.1%
3670 16
 
< 0.1%
4093 201
< 0.1%
ValueCountFrequency (%)
734781 484
 
< 0.1%
537573 2589
0.2%
470140 3028
0.3%
405926 2346
0.2%
353089 2562
0.2%
298679 2187
0.2%
279426 1439
0.1%
271662 1490
0.1%
263514 1365
0.1%
253959 1008
 
0.1%

k-관리비부과면적
Real number (ℝ)

MISSING 

Distinct735
Distinct (%)0.3%
Missing869563
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean120726.49
Minimum0
Maximum969877
Zeros1067
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:09.786823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17152
Q140735
median78125
Q3159544
95-th percentile379722
Maximum969877
Range969877
Interquartile range (IQR)118809

Descriptive statistics

Standard deviation129020.28
Coefficient of variation (CV)1.068699
Kurtosis8.100548
Mean120726.49
Median Absolute Deviation (MAD)42568
Skewness2.5512615
Sum3.0092164 × 1010
Variance1.6646232 × 1010
MonotonicityNot monotonic
2024-07-10T16:55:09.845136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618666 3028
 
0.3%
240629 2816
 
0.3%
646257 2589
 
0.2%
470732 2562
 
0.2%
166985 2465
 
0.2%
232099 2360
 
0.2%
520773 2346
 
0.2%
205962 2278
 
0.2%
379722 2187
 
0.2%
257942 2118
 
0.2%
Other values (725) 224510
 
20.1%
(Missing) 869563
77.7%
ValueCountFrequency (%)
0 1067
0.1%
3778 29
 
< 0.1%
4434 134
 
< 0.1%
4545 147
 
< 0.1%
4947 90
 
< 0.1%
5134 84
 
< 0.1%
5316 134
 
< 0.1%
5501 16
 
< 0.1%
6581 62
 
< 0.1%
6753 83
 
< 0.1%
ValueCountFrequency (%)
969877 484
 
< 0.1%
646257 2589
0.2%
618666 3028
0.3%
520773 2346
0.2%
470732 2562
0.2%
379722 2187
0.2%
362548 1490
0.1%
361341 36
 
< 0.1%
345648 1365
0.1%
327243 1008
 
0.1%

k-전용면적별세대현황(60㎡이하)
Real number (ℝ)

MISSING  ZEROS 

Distinct348
Distinct (%)0.1%
Missing869608
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean477.91284
Minimum0
Maximum4975
Zeros53781
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:09.901474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148
median225
Q3576
95-th percentile1716
Maximum4975
Range4975
Interquartile range (IQR)528

Descriptive statistics

Standard deviation759.9094
Coefficient of variation (CV)1.5900586
Kurtosis15.135865
Mean477.91284
Median Absolute Deviation (MAD)225
Skewness3.5029503
Sum1.1910257 × 108
Variance577462.3
MonotonicityNot monotonic
2024-07-10T16:55:09.958725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53781
 
4.8%
1150 3028
 
0.3%
370 2875
 
0.3%
4975 2816
 
0.3%
750 2730
 
0.2%
2433 2465
 
0.2%
3710 2360
 
0.2%
683 2346
 
0.2%
512 2308
 
0.2%
827 2278
 
0.2%
Other values (338) 172227
 
15.4%
(Missing) 869608
77.7%
ValueCountFrequency (%)
0 53781
4.8%
1 165
 
< 0.1%
2 60
 
< 0.1%
3 84
 
< 0.1%
9 1
 
< 0.1%
10 180
 
< 0.1%
11 89
 
< 0.1%
12 359
 
< 0.1%
14 488
 
< 0.1%
15 263
 
< 0.1%
ValueCountFrequency (%)
4975 2816
0.3%
3710 2360
0.2%
2854 484
 
< 0.1%
2433 2465
0.2%
2256 1964
0.2%
1786 2112
0.2%
1716 1757
0.2%
1710 1823
0.2%
1687 97
 
< 0.1%
1632 115
 
< 0.1%

k-전용면적별세대현황(60㎡~85㎡이하)
Real number (ℝ)

MISSING  ZEROS 

Distinct387
Distinct (%)0.2%
Missing869608
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean476.71344
Minimum0
Maximum5132
Zeros42969
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:10.014364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q195
median256
Q3582
95-th percentile1500
Maximum5132
Range5132
Interquartile range (IQR)487

Descriptive statistics

Standard deviation727.55357
Coefficient of variation (CV)1.5261864
Kurtosis16.61204
Mean476.71344
Median Absolute Deviation (MAD)206
Skewness3.7380935
Sum1.1880366 × 108
Variance529334.2
MonotonicityNot monotonic
2024-07-10T16:55:10.071739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42969
 
3.8%
688 3279
 
0.3%
4042 3028
 
0.3%
65 2969
 
0.3%
1500 2589
 
0.2%
4424 2562
 
0.2%
342 2412
 
0.2%
1363 2346
 
0.2%
330 2223
 
0.2%
936 2187
 
0.2%
Other values (377) 182650
 
16.3%
(Missing) 869608
77.7%
ValueCountFrequency (%)
0 42969
3.8%
1 324
 
< 0.1%
2 184
 
< 0.1%
5 43
 
< 0.1%
7 268
 
< 0.1%
12 168
 
< 0.1%
14 283
 
< 0.1%
17 158
 
< 0.1%
20 140
 
< 0.1%
22 523
 
< 0.1%
ValueCountFrequency (%)
5132 484
 
< 0.1%
4424 2562
0.2%
4042 3028
0.3%
2092 2118
0.2%
1657 1490
0.1%
1606 1057
 
0.1%
1500 2589
0.2%
1484 1558
0.1%
1363 2346
0.2%
1340 131
 
< 0.1%

k-85㎡~135㎡이하
Real number (ℝ)

MISSING  ZEROS 

Distinct244
Distinct (%)0.1%
Missing869608
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean167.52847
Minimum0
Maximum1500
Zeros99459
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:10.126945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median63
Q3237
95-th percentile600
Maximum1500
Range1500
Interquartile range (IQR)237

Descriptive statistics

Standard deviation248.92814
Coefficient of variation (CV)1.4858856
Kurtosis6.5914619
Mean167.52847
Median Absolute Deviation (MAD)63
Skewness2.2901314
Sum41750440
Variance61965.22
MonotonicityNot monotonic
2024-07-10T16:55:10.184210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99459
 
8.9%
90 3225
 
0.3%
486 3028
 
0.3%
900 2589
 
0.2%
456 2554
 
0.2%
340 2346
 
0.2%
1402 2187
 
0.2%
513 2109
 
0.2%
118 2093
 
0.2%
575 1643
 
0.1%
Other values (234) 127981
 
11.4%
(Missing) 869608
77.7%
ValueCountFrequency (%)
0 99459
8.9%
1 249
 
< 0.1%
2 12
 
< 0.1%
3 234
 
< 0.1%
4 119
 
< 0.1%
5 1108
 
0.1%
8 190
 
< 0.1%
9 32
 
< 0.1%
10 154
 
< 0.1%
12 936
 
0.1%
ValueCountFrequency (%)
1500 484
 
< 0.1%
1402 2187
0.2%
1020 258
 
< 0.1%
966 912
 
0.1%
914 1365
0.1%
900 2589
0.2%
843 1008
 
0.1%
787 711
 
0.1%
680 276
 
< 0.1%
606 1496
0.1%

k-135㎡초과
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing1118495
Missing (%)> 99.9%
Memory size8.5 MiB
70.0
327 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1308
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70.0
2nd row70.0
3rd row70.0
4th row70.0
5th row70.0

Common Values

ValueCountFrequency (%)
70.0 327
 
< 0.1%
(Missing) 1118495
> 99.9%

Length

2024-07-10T16:55:10.238375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:10.275736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
70.0 327
100.0%

Most occurring characters

ValueCountFrequency (%)
0 654
50.0%
7 327
25.0%
. 327
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 654
50.0%
7 327
25.0%
. 327
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 654
50.0%
7 327
25.0%
. 327
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 654
50.0%
7 327
25.0%
. 327
25.0%

k-홈페이지
Text

MISSING 

Distinct221
Distinct (%)0.2%
Missing1005647
Missing (%)89.9%
Memory size8.5 MiB
2024-07-10T16:55:10.338463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length54
Median length36
Mean length17.146181
Min length1

Characters and Unicode

Total characters1940519
Distinct characters217
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowtest
2nd rowtest
3rd rowtest
4th rowtest
5th rowtest
ValueCountFrequency (%)
없음 3606
 
3.2%
www.jsls.co.kr 3028
 
2.7%
gaepo001@naver.com 2816
 
2.5%
www.oftapt.com 2589
 
2.3%
성산시영.apt.co.kr 2360
 
2.1%
bpxi.aptner.com 2346
 
2.1%
정릉풍림.apti.co.kr 2278
 
2.0%
www.rexleapt.com 2187
 
1.9%
www.ydpprugio.com 2118
 
1.9%
cd3.apti.co.kr 2112
 
1.9%
Other values (212) 88685
77.7%
2024-07-10T16:55:10.485850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 254660
 
13.1%
a 132631
 
6.8%
o 129874
 
6.7%
c 123051
 
6.3%
w 117422
 
6.1%
r 99412
 
5.1%
t 98313
 
5.1%
p 91747
 
4.7%
e 81162
 
4.2%
m 76359
 
3.9%
Other values (207) 735888
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1940519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 254660
 
13.1%
a 132631
 
6.8%
o 129874
 
6.7%
c 123051
 
6.3%
w 117422
 
6.1%
r 99412
 
5.1%
t 98313
 
5.1%
p 91747
 
4.7%
e 81162
 
4.2%
m 76359
 
3.9%
Other values (207) 735888
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1940519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 254660
 
13.1%
a 132631
 
6.8%
o 129874
 
6.7%
c 123051
 
6.3%
w 117422
 
6.1%
r 99412
 
5.1%
t 98313
 
5.1%
p 91747
 
4.7%
e 81162
 
4.2%
m 76359
 
3.9%
Other values (207) 735888
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1940519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 254660
 
13.1%
a 132631
 
6.8%
o 129874
 
6.7%
c 123051
 
6.3%
w 117422
 
6.1%
r 99412
 
5.1%
t 98313
 
5.1%
p 91747
 
4.7%
e 81162
 
4.2%
m 76359
 
3.9%
Other values (207) 735888
37.9%

k-등록일자
Date

MISSING 

Distinct126
Distinct (%)1.1%
Missing1107832
Missing (%)99.0%
Memory size8.5 MiB
Minimum2017-02-01 10:49:21
Maximum2023-05-12 10:09:44
2024-07-10T16:55:10.557765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:55:10.618516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

k-수정일자
Date

MISSING 

Distinct743
Distinct (%)0.3%
Missing869608
Missing (%)77.7%
Memory size8.5 MiB
Minimum2020-02-17 04:28:42
Maximum2023-09-26 12:46:39
2024-07-10T16:55:10.677922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:55:10.739016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct526
Distinct (%)0.3%
Missing913304
Missing (%)81.6%
Memory size8.5 MiB
2024-07-10T16:55:10.826548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length20
Median length11
Mean length12.051485
Min length1

Characters and Unicode

Total characters2476797
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row21380015910
2nd row21380015910
3rd row21380015910
4th row21380015910
5th row21380015910
ValueCountFrequency (%)
909-01-23103-1 3028
 
1.5%
21380014370 2816
 
1.4%
908-00-99517-1 2562
 
1.2%
907-009-67911 2465
 
1.2%
907-00-16928-1 2360
 
1.1%
90900004511 2346
 
1.1%
20980031540 2278
 
1.1%
91201768311 2187
 
1.1%
90700067131 2112
 
1.0%
209-80-03061-0 2109
 
1.0%
Other values (518) 181615
88.2%
2024-07-10T16:55:10.988431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 615774
24.9%
1 396753
16.0%
9 267009
10.8%
- 218151
 
8.8%
2 188246
 
7.6%
7 175999
 
7.1%
8 163532
 
6.6%
6 134487
 
5.4%
3 122755
 
5.0%
4 99094
 
4.0%
Other values (2) 94997
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2476797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 615774
24.9%
1 396753
16.0%
9 267009
10.8%
- 218151
 
8.8%
2 188246
 
7.6%
7 175999
 
7.1%
8 163532
 
6.6%
6 134487
 
5.4%
3 122755
 
5.0%
4 99094
 
4.0%
Other values (2) 94997
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2476797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 615774
24.9%
1 396753
16.0%
9 267009
10.8%
- 218151
 
8.8%
2 188246
 
7.6%
7 175999
 
7.1%
8 163532
 
6.6%
6 134487
 
5.4%
3 122755
 
5.0%
4 99094
 
4.0%
Other values (2) 94997
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2476797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 615774
24.9%
1 396753
16.0%
9 267009
10.8%
- 218151
 
8.8%
2 188246
 
7.6%
7 175999
 
7.1%
8 163532
 
6.6%
6 134487
 
5.4%
3 122755
 
5.0%
4 99094
 
4.0%
Other values (2) 94997
 
3.8%

경비비관리형태
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing870988
Missing (%)77.8%
Memory size8.5 MiB
위탁
206401 
직영
33526 
위탁+직영
 
6108
기타
 
1799

Length

Max length5
Median length2
Mean length2.0739366
Min length2

Characters and Unicode

Total characters513992
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row직영
2nd row직영
3rd row직영
4th row직영
5th row직영

Common Values

ValueCountFrequency (%)
위탁 206401
 
18.4%
직영 33526
 
3.0%
위탁+직영 6108
 
0.5%
기타 1799
 
0.2%
(Missing) 870988
77.8%

Length

2024-07-10T16:55:11.062993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:11.104028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
위탁 206401
83.3%
직영 33526
 
13.5%
위탁+직영 6108
 
2.5%
기타 1799
 
0.7%

Most occurring characters

ValueCountFrequency (%)
212509
41.3%
212509
41.3%
39634
 
7.7%
39634
 
7.7%
+ 6108
 
1.2%
1799
 
0.4%
1799
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 513992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
212509
41.3%
212509
41.3%
39634
 
7.7%
39634
 
7.7%
+ 6108
 
1.2%
1799
 
0.4%
1799
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 513992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
212509
41.3%
212509
41.3%
39634
 
7.7%
39634
 
7.7%
+ 6108
 
1.2%
1799
 
0.4%
1799
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 513992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
212509
41.3%
212509
41.3%
39634
 
7.7%
39634
 
7.7%
+ 6108
 
1.2%
1799
 
0.4%
1799
 
0.4%

세대전기계약방법
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing878747
Missing (%)78.5%
Memory size8.5 MiB
종합계약
122973 
단일계약
117102 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters960300
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row단일계약
2nd row단일계약
3rd row단일계약
4th row단일계약
5th row단일계약

Common Values

ValueCountFrequency (%)
종합계약 122973
 
11.0%
단일계약 117102
 
10.5%
(Missing) 878747
78.5%

Length

2024-07-10T16:55:11.148644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:11.185803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
종합계약 122973
51.2%
단일계약 117102
48.8%

Most occurring characters

ValueCountFrequency (%)
240075
25.0%
240075
25.0%
122973
12.8%
122973
12.8%
117102
12.2%
117102
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 960300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
240075
25.0%
240075
25.0%
122973
12.8%
122973
12.8%
117102
12.2%
117102
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 960300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
240075
25.0%
240075
25.0%
122973
12.8%
122973
12.8%
117102
12.2%
117102
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 960300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
240075
25.0%
240075
25.0%
122973
12.8%
122973
12.8%
117102
12.2%
117102
12.2%

청소비관리형태
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing871178
Missing (%)77.9%
Memory size8.5 MiB
위탁
225016 
직영
 
15052
위탁+직영
 
4127
기타
 
3449

Length

Max length5
Median length2
Mean length2.0499952
Min length2

Characters and Unicode

Total characters507669
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row직영
2nd row직영
3rd row직영
4th row직영
5th row직영

Common Values

ValueCountFrequency (%)
위탁 225016
 
20.1%
직영 15052
 
1.3%
위탁+직영 4127
 
0.4%
기타 3449
 
0.3%
(Missing) 871178
77.9%

Length

2024-07-10T16:55:11.230423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:11.272067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
위탁 225016
90.9%
직영 15052
 
6.1%
위탁+직영 4127
 
1.7%
기타 3449
 
1.4%

Most occurring characters

ValueCountFrequency (%)
229143
45.1%
229143
45.1%
19179
 
3.8%
19179
 
3.8%
+ 4127
 
0.8%
3449
 
0.7%
3449
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 507669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
229143
45.1%
229143
45.1%
19179
 
3.8%
19179
 
3.8%
+ 4127
 
0.8%
3449
 
0.7%
3449
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 507669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
229143
45.1%
229143
45.1%
19179
 
3.8%
19179
 
3.8%
+ 4127
 
0.8%
3449
 
0.7%
3449
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 507669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
229143
45.1%
229143
45.1%
19179
 
3.8%
19179
 
3.8%
+ 4127
 
0.8%
3449
 
0.7%
3449
 
0.7%

건축면적
Real number (ℝ)

MISSING  ZEROS 

Distinct455
Distinct (%)0.2%
Missing869714
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean189507.01
Minimum0
Maximum31596200
Zeros117399
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:11.320296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1710.55
Q38414.21
95-th percentile49266.41
Maximum31596200
Range31596200
Interquartile range (IQR)8414.21

Descriptive statistics

Standard deviation1729026.8
Coefficient of variation (CV)9.1238147
Kurtosis163.15343
Mean189507.01
Median Absolute Deviation (MAD)1710.55
Skewness12.171983
Sum4.7207712 × 1010
Variance2.9895337 × 1012
MonotonicityNot monotonic
2024-07-10T16:55:11.377023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117399
 
10.5%
49266.41 2589
 
0.2%
172371 2465
 
0.2%
25737 2346
 
0.2%
20457.82 2187
 
0.2%
11358.35 2093
 
0.2%
14618.59 1964
 
0.2%
6725 1823
 
0.2%
13512.33 1654
 
0.1%
10673.96 1526
 
0.1%
Other values (445) 113062
 
10.1%
(Missing) 869714
77.7%
ValueCountFrequency (%)
0 117399
10.5%
2.15 294
 
< 0.1%
2.35 160
 
< 0.1%
114 193
 
< 0.1%
369.4 134
 
< 0.1%
607.12 29
 
< 0.1%
632.65 135
 
< 0.1%
678.48 83
 
< 0.1%
710.06 201
 
< 0.1%
731.53 134
 
< 0.1%
ValueCountFrequency (%)
31596200 71
 
< 0.1%
24041400 857
0.1%
17598789 188
 
< 0.1%
8286488 529
< 0.1%
8110555 584
0.1%
6562360 432
< 0.1%
5834339 332
 
< 0.1%
5261328 827
0.1%
3320457 96
 
< 0.1%
1392874 98
 
< 0.1%

주차대수
Real number (ℝ)

MISSING  ZEROS 

Distinct526
Distinct (%)0.2%
Missing869714
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean1063.6788
Minimum0
Maximum12096
Zeros17945
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:11.435033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1315
median683
Q31274
95-th percentile4023
Maximum12096
Range12096
Interquartile range (IQR)959

Descriptive statistics

Standard deviation1235.4376
Coefficient of variation (CV)1.1614762
Kurtosis14.51695
Mean1063.6788
Median Absolute Deviation (MAD)443
Skewness2.903546
Sum2.6497089 × 108
Variance1526306.1
MonotonicityNot monotonic
2024-07-10T16:55:11.493248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17945
 
1.6%
2 3413
 
0.3%
1 2852
 
0.3%
4245 2589
 
0.2%
3021 2562
 
0.2%
930 2465
 
0.2%
1217 2360
 
0.2%
6075 2346
 
0.2%
4443 2187
 
0.2%
950 2130
 
0.2%
Other values (516) 208259
 
18.6%
(Missing) 869714
77.7%
ValueCountFrequency (%)
0 17945
1.6%
1 2852
 
0.3%
2 3413
 
0.3%
3 134
 
< 0.1%
4 130
 
< 0.1%
8 85
 
< 0.1%
23 286
 
< 0.1%
38 134
 
< 0.1%
45 82
 
< 0.1%
50 147
 
< 0.1%
ValueCountFrequency (%)
12096 484
 
< 0.1%
6075 2346
0.2%
4890 1490
0.1%
4443 2187
0.2%
4368 1439
0.1%
4245 2589
0.2%
4190 1365
0.1%
4023 1008
 
0.1%
3961 79
 
< 0.1%
3716 475
 
< 0.1%

기타/의무/임대/임의=1/2/3/4
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing869563
Missing (%)77.7%
Memory size8.5 MiB
의무
239198 
기타
 
4177
임의
 
3682
임대
 
2202

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters498518
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row임의
2nd row임의
3rd row임의
4th row임의
5th row임의

Common Values

ValueCountFrequency (%)
의무 239198
 
21.4%
기타 4177
 
0.4%
임의 3682
 
0.3%
임대 2202
 
0.2%
(Missing) 869563
77.7%

Length

2024-07-10T16:55:11.544919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-10T16:55:11.584746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
의무 239198
96.0%
기타 4177
 
1.7%
임의 3682
 
1.5%
임대 2202
 
0.9%

Most occurring characters

ValueCountFrequency (%)
242880
48.7%
239198
48.0%
5884
 
1.2%
4177
 
0.8%
4177
 
0.8%
2202
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 498518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
242880
48.7%
239198
48.0%
5884
 
1.2%
4177
 
0.8%
4177
 
0.8%
2202
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 498518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
242880
48.7%
239198
48.0%
5884
 
1.2%
4177
 
0.8%
4177
 
0.8%
2202
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 498518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
242880
48.7%
239198
48.0%
5884
 
1.2%
4177
 
0.8%
4177
 
0.8%
2202
 
0.4%

단지승인일
Date

MISSING 

Distinct735
Distinct (%)0.3%
Missing870286
Missing (%)77.8%
Memory size8.5 MiB
Minimum1982-09-18 00:00:00
Maximum2023-08-04 15:39:34
2024-07-10T16:55:11.634097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:55:11.696982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용허가여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing869563
Missing (%)77.7%
Memory size2.1 MiB
True
249259 
(Missing)
869563 
ValueCountFrequency (%)
True 249259
 
22.3%
(Missing) 869563
77.7%
2024-07-10T16:55:11.742797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

관리비 업로드
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing869563
Missing (%)77.7%
Memory size2.1 MiB
False
245117 
True
 
4142
(Missing)
869563 
ValueCountFrequency (%)
False 245117
 
21.9%
True 4142
 
0.4%
(Missing) 869563
77.7%
2024-07-10T16:55:11.772737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

좌표X
Real number (ℝ)

MISSING 

Distinct741
Distinct (%)0.3%
Missing869670
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean126.99523
Minimum126.79832
Maximum127.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:11.817469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum126.79832
5-th percentile126.83685
Q1126.91316
median127.01497
Q3127.05959
95-th percentile127.13659
Maximum127.18
Range0.3816791
Interquartile range (IQR)0.1464328

Descriptive statistics

Standard deviation0.09104463
Coefficient of variation (CV)0.00071691379
Kurtosis-0.92388887
Mean126.99523
Median Absolute Deviation (MAD)0.0652707
Skewness-0.18526185
Sum31641115
Variance0.0082891247
MonotonicityNot monotonic
2024-07-10T16:55:11.876809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.088451 3028
 
0.3%
127.0585214 2816
 
0.3%
127.1143761 2589
 
0.2%
127.0650701 2562
 
0.2%
127.0665685 2465
 
0.2%
126.9028201 2360
 
0.2%
127.0135922 2346
 
0.2%
127.0073229 2278
 
0.2%
127.0489846 2187
 
0.2%
126.9045485 2118
 
0.2%
Other values (731) 224403
 
20.1%
(Missing) 869670
77.7%
ValueCountFrequency (%)
126.7983185 138
 
< 0.1%
126.80664 1053
0.1%
126.8141297 276
 
< 0.1%
126.8158189 190
 
< 0.1%
126.8174976 121
 
< 0.1%
126.8184548 44
 
< 0.1%
126.818592 13
 
< 0.1%
126.8192912 159
 
< 0.1%
126.8193263 226
 
< 0.1%
126.819535 110
 
< 0.1%
ValueCountFrequency (%)
127.1799976 254
< 0.1%
127.1784546 330
< 0.1%
127.1782697 357
< 0.1%
127.1778329 140
 
< 0.1%
127.1772605 293
< 0.1%
127.1767916 390
< 0.1%
127.1766546 217
< 0.1%
127.1762695 268
< 0.1%
127.1751069 477
< 0.1%
127.1747855 507
< 0.1%

좌표Y
Real number (ℝ)

MISSING 

Distinct741
Distinct (%)0.3%
Missing869670
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean37.545785
Minimum37.447843
Maximum37.687725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:11.936215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum37.447843
5-th percentile37.477683
Q137.499201
median37.544936
Q337.577117
95-th percentile37.645483
Maximum37.687725
Range0.239882
Interquartile range (IQR)0.077916

Descriptive statistics

Standard deviation0.052483235
Coefficient of variation (CV)0.0013978463
Kurtosis-0.59649551
Mean37.545785
Median Absolute Deviation (MAD)0.0408361
Skewness0.43595515
Sum9354607.3
Variance0.00275449
MonotonicityNot monotonic
2024-07-10T16:55:11.994209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5127515 3028
 
0.3%
37.4800022 2816
 
0.3%
37.4885215 2589
 
0.2%
37.4977415 2562
 
0.2%
37.6427243 2465
 
0.2%
37.5684652 2360
 
0.2%
37.507538 2346
 
0.2%
37.6185895 2278
 
0.2%
37.4930287 2187
 
0.2%
37.5120598 2118
 
0.2%
Other values (731) 224403
 
20.1%
(Missing) 869670
77.7%
ValueCountFrequency (%)
37.4478428 757
0.1%
37.4479487 178
 
< 0.1%
37.44810012 80
 
< 0.1%
37.45014991 124
 
< 0.1%
37.4511442 199
 
< 0.1%
37.4511481 458
< 0.1%
37.452828 193
 
< 0.1%
37.4545977 55
 
< 0.1%
37.45470291 150
 
< 0.1%
37.4550374 125
 
< 0.1%
ValueCountFrequency (%)
37.6877248 300
 
< 0.1%
37.6816146 270
 
< 0.1%
37.6773823 597
 
0.1%
37.6734511 1757
0.2%
37.66744 36
 
< 0.1%
37.6674029 341
 
< 0.1%
37.667052 83
 
< 0.1%
37.6655235 912
0.1%
37.6632392 758
0.1%
37.6632213 468
 
< 0.1%

단지신청일
Date

MISSING 

Distinct259
Distinct (%)0.1%
Missing869625
Missing (%)77.7%
Memory size8.5 MiB
Minimum2013-03-07 09:46:12
Maximum2023-08-04 15:31:27
2024-07-10T16:55:12.050794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:55:12.110101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

target
Real number (ℝ)

Distinct14530
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57991.532
Minimum350
Maximum1450000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2024-07-10T16:55:12.170558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile17000
Q130500
median44800
Q369800
95-th percentile143500
Maximum1450000
Range1449650
Interquartile range (IQR)39300

Descriptive statistics

Standard deviation46426.022
Coefficient of variation (CV)0.80056553
Kurtosis25.465357
Mean57991.532
Median Absolute Deviation (MAD)17200
Skewness3.5176408
Sum6.4882202 × 1010
Variance2.1553755 × 109
MonotonicityNot monotonic
2024-07-10T16:55:12.229376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 11685
 
1.0%
40000 10611
 
0.9%
30000 10046
 
0.9%
35000 9595
 
0.9%
50000 9393
 
0.8%
45000 9074
 
0.8%
32000 8509
 
0.8%
38000 7926
 
0.7%
42000 7861
 
0.7%
43000 7820
 
0.7%
Other values (14520) 1026302
91.7%
ValueCountFrequency (%)
350 1
< 0.1%
500 2
< 0.1%
600 1
< 0.1%
630 1
< 0.1%
660 1
< 0.1%
700 1
< 0.1%
710 2
< 0.1%
730 2
< 0.1%
790 1
< 0.1%
800 1
< 0.1%
ValueCountFrequency (%)
1450000 1
< 0.1%
1350000 1
< 0.1%
1300000 1
< 0.1%
1200000 1
< 0.1%
1170000 1
< 0.1%
1150000 2
< 0.1%
1100000 2
< 0.1%
1080000 1
< 0.1%
1053000 1
< 0.1%
1000000 2
< 0.1%

계약연도
Categorical

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2015
119891 
2017
104893 
2016
99253 
2014
85130 
2020
83711 
Other values (12)
625944 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4475288
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2015 119891
10.7%
2017 104893
 
9.4%
2016 99253
 
8.9%
2014 85130
 
7.6%
2020 83711
 
7.5%
2018 81413
 
7.3%
2019 74696
 
6.7%
2009 73491
 
6.6%
2013 67865
 
6.1%
2007 58767
 
5.3%
Other values (7) 269712
24.1%

Length

2024-07-10T16:55:12.283209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015 119891
10.7%
2017 104893
 
9.4%
2016 99253
 
8.9%
2014 85130
 
7.6%
2020 83711
 
7.5%
2018 81413
 
7.3%
2019 74696
 
6.7%
2009 73491
 
6.6%
2013 67865
 
6.1%
2007 58767
 
5.3%
Other values (7) 269712
24.1%

Most occurring characters

ValueCountFrequency (%)
0 1436260
32.1%
2 1328477
29.7%
1 870592
19.5%
7 163660
 
3.7%
9 148187
 
3.3%
8 138425
 
3.1%
5 119891
 
2.7%
6 99253
 
2.2%
3 85413
 
1.9%
4 85130
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4475288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1436260
32.1%
2 1328477
29.7%
1 870592
19.5%
7 163660
 
3.7%
9 148187
 
3.3%
8 138425
 
3.1%
5 119891
 
2.7%
6 99253
 
2.2%
3 85413
 
1.9%
4 85130
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4475288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1436260
32.1%
2 1328477
29.7%
1 870592
19.5%
7 163660
 
3.7%
9 148187
 
3.3%
8 138425
 
3.1%
5 119891
 
2.7%
6 99253
 
2.2%
3 85413
 
1.9%
4 85130
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4475288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1436260
32.1%
2 1328477
29.7%
1 870592
19.5%
7 163660
 
3.7%
9 148187
 
3.3%
8 138425
 
3.1%
5 119891
 
2.7%
6 99253
 
2.2%
3 85413
 
1.9%
4 85130
 
1.9%

계약월
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
06
112437 
03
106936 
07
105560 
05
99429 
08
96871 
Other values (7)
597589 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2237644
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row01
5th row01

Common Values

ValueCountFrequency (%)
06 112437
10.0%
03 106936
9.6%
07 105560
9.4%
05 99429
8.9%
08 96871
8.7%
04 95160
8.5%
10 92922
8.3%
02 89646
8.0%
01 87017
7.8%
09 79467
7.1%
Other values (2) 153377
13.7%

Length

2024-07-10T16:55:12.328031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06 112437
10.0%
03 106936
9.6%
07 105560
9.4%
05 99429
8.9%
08 96871
8.7%
04 95160
8.5%
10 92922
8.3%
02 89646
8.0%
01 87017
7.8%
09 79467
7.1%
Other values (2) 153377
13.7%

Most occurring characters

ValueCountFrequency (%)
0 965445
43.1%
1 411124
18.4%
2 165215
 
7.4%
6 112437
 
5.0%
3 106936
 
4.8%
7 105560
 
4.7%
5 99429
 
4.4%
8 96871
 
4.3%
4 95160
 
4.3%
9 79467
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2237644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 965445
43.1%
1 411124
18.4%
2 165215
 
7.4%
6 112437
 
5.0%
3 106936
 
4.8%
7 105560
 
4.7%
5 99429
 
4.4%
8 96871
 
4.3%
4 95160
 
4.3%
9 79467
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2237644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 965445
43.1%
1 411124
18.4%
2 165215
 
7.4%
6 112437
 
5.0%
3 106936
 
4.8%
7 105560
 
4.7%
5 99429
 
4.4%
8 96871
 
4.3%
4 95160
 
4.3%
9 79467
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2237644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 965445
43.1%
1 411124
18.4%
2 165215
 
7.4%
6 112437
 
5.0%
3 106936
 
4.8%
7 105560
 
4.7%
5 99429
 
4.4%
8 96871
 
4.3%
4 95160
 
4.3%
9 79467
 
3.6%
Distinct5993
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
Minimum2007-01-01 00:00:00
Maximum2023-06-30 00:00:00
2024-07-10T16:55:12.378531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:55:12.439988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-07-10T16:54:51.087760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:25.464087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:27.016720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:28.810073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:30.274026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:31.776774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:33.375958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:34.767798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:35.761065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:36.797904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:38.123892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:39.228349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:40.347293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:41.542420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:42.656245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:43.978234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:45.138081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:46.283474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:47.402953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:48.590600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:49.976349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:51.184096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:25.624907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:27.104674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:28.906641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:30.372640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:31.879800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:33.470807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:34.813182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:35.810938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:36.853063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:38.174573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:39.282223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:40.401798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:41.595263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:42.708325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:44.028293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:45.193360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:46.335894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:47.455002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:48.648100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:50.027555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:51.272807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:25.757629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:27.193755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:29.003326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:30.466494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:31.975193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:33.560831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:34.857635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:35.860074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:36.904340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:38.224950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:39.333814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:40.456496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:41.649164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:42.759959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:44.081560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:45.247546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:46.388041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:47.506855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:48.702433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-10T16:54:39.859531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:41.003170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:42.172599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:43.490922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:44.646263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:45.784947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:46.916779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:48.061334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:49.246738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:50.582131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:51.970029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:26.488240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-10T16:54:31.201998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-10T16:54:35.380477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:36.395546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:37.651694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:38.799526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:39.911339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:41.057486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:42.225270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:43.543824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:44.700100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:45.840960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:46.970090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:48.115052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:49.302973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:50.633103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:52.023769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:26.543751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:27.960500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:29.802657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:31.261508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:32.755892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:34.349930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:35.427727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:36.444276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:37.706211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:38.852617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:39.964565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:41.144649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:42.279596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:43.596036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:44.751997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:45.895008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:47.023558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:48.167937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:49.357822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:50.682583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:52.076072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-10T16:54:26.598852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-10T16:54:29.915218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-10T16:54:40.294044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-10T16:54:50.993063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-10T16:55:03.269825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구번지본번부번아파트명전용면적(㎡)계약년월계약일건축년도도로명해제사유발생일등기신청일자거래유형중개사소재지k-단지분류(아파트,주상복합등등)k-전화번호k-팩스번호단지소개기존clobk-세대타입(분양형태)k-관리방식k-복도유형k-난방방식k-전체동수k-전체세대수k-건설사(시공사)k-시행사k-사용검사일-사용승인일k-연면적k-주거전용면적k-관리비부과면적k-전용면적별세대현황(60㎡이하)k-전용면적별세대현황(60㎡~85㎡이하)k-85㎡~135㎡이하k-135㎡초과k-홈페이지k-등록일자k-수정일자고용보험관리번호경비비관리형태세대전기계약방법청소비관리형태건축면적주차대수기타/의무/임대/임의=1/2/3/4단지승인일사용허가여부관리비 업로드좌표X좌표Y단지신청일target계약연도계약월계약년월일
0서울특별시 강남구 개포동658-1658.01.0개포6차우성79.972017120831987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01240002017122017-12-08
1서울특별시 강남구 개포동658-1658.01.0개포6차우성79.972017122241987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01235002017122017-12-22
2서울특별시 강남구 개포동658-1658.01.0개포6차우성54.982017122851987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.0915002017122017-12-28
3서울특별시 강남구 개포동658-1658.01.0개포6차우성79.972018010341987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01300002018012018-01-03
4서울특별시 강남구 개포동658-1658.01.0개포6차우성79.972018010821987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01170002018012018-01-08
5서울특별시 강남구 개포동658-1658.01.0개포6차우성79.972018011111987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01300002018012018-01-11
6서울특별시 강남구 개포동658-1658.01.0개포6차우성79.972018031921987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01395002018032018-03-19
7서울특별시 강남구 개포동658-1658.01.0개포6차우성54.982018040551987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01075002018042018-04-05
8서울특별시 강남구 개포동658-1658.01.0개포6차우성79.972018062831987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01450002018062018-06-28
9서울특별시 강남구 개포동658-1658.01.0개포6차우성54.982018070931987언주로 3NaN--아파트025776611025776673NaN분양자치관리계단식개별난방8.0270.0우성건설모름1987-11-21 00:00:00.022637.020204.022637.020.0250.00.0NaNNaN2022-11-09 20:10:43.02023-09-23 17:21:41.0NaN직영단일계약직영4858.0262.0임의2022-11-17 13:00:29.0YN127.0572137.4767632022-11-17 10:19:06.01120002018072018-07-09
시군구번지본번부번아파트명전용면적(㎡)계약년월계약일건축년도도로명해제사유발생일등기신청일자거래유형중개사소재지k-단지분류(아파트,주상복합등등)k-전화번호k-팩스번호단지소개기존clobk-세대타입(분양형태)k-관리방식k-복도유형k-난방방식k-전체동수k-전체세대수k-건설사(시공사)k-시행사k-사용검사일-사용승인일k-연면적k-주거전용면적k-관리비부과면적k-전용면적별세대현황(60㎡이하)k-전용면적별세대현황(60㎡~85㎡이하)k-85㎡~135㎡이하k-135㎡초과k-홈페이지k-등록일자k-수정일자고용보험관리번호경비비관리형태세대전기계약방법청소비관리형태건축면적주차대수기타/의무/임대/임의=1/2/3/4단지승인일사용허가여부관리비 업로드좌표X좌표Y단지신청일target계약연도계약월계약년월일
1118812서울특별시 은평구 구산동382382.00.0갈현현대59.942007061631998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0160002007062007-06-16
1118813서울특별시 은평구 구산동382382.00.0갈현현대114.952007062651998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0340002007062007-06-26
1118814서울특별시 은평구 구산동382382.00.0갈현현대59.942007070431998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0166002007072007-07-04
1118815서울특별시 은평구 구산동382382.00.0갈현현대84.8320070705181998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0289002007072007-07-05
1118816서울특별시 은평구 구산동382382.00.0갈현현대59.942007070791998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0179002007072007-07-07
1118817서울특별시 은평구 구산동382382.00.0갈현현대59.9420070712111998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0200002007072007-07-12
1118818서울특별시 은평구 구산동382382.00.0갈현현대59.9420070825101998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0200002007082007-08-25
1118819서울특별시 은평구 구산동382382.00.0갈현현대84.8320070831201998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0280002007082007-08-31
1118820서울특별시 은평구 구산동382382.00.0갈현현대84.832007091581998서오릉로21길 36NaN--아파트23547358.023529429.0NaN분양위탁관리혼합식개별난방4.0366.0현대건설갈현아파트재건축조합1998-11-28 00:00:00.045515.028335.028335.0171.0141.054.0NaNNaNNaN2023-09-26 07:15:20.0911-01-25120-1위탁종합계약위탁0.0366.0의무2013-06-04 16:18:51.0YN126.90563837.6129622013-03-07 09:46:27.0290002007092007-09-15
1118821서울특별시 중구 묵정동11-6711.067.0묵정52.462007011051981서애로1길 34NaN--아파트222722129.0222723129.0NaN분양자치관리복도식개별난방1.0122.0라이프주택묵정아파트 주택조합1981-05-25 00:00:00.07354.06455.06912.0121.00.01.0NaNNaN2017-09-05 20:06:39.02023-09-26 04:36:29.02018002485직영종합계약직영7354.045.0임의2020-07-10 00:00:00.0YY127.00007137.5607062017-09-05 20:06:39.0132502007012007-01-10